Automated Machine Learning for High-Resolution Daily and Hourly Methane Emission Mapping for Rice Paddies over South Korea: Integrating MODIS, ERA5-Land, and Soil Data.
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| Title: | Automated Machine Learning for High-Resolution Daily and Hourly Methane Emission Mapping for Rice Paddies over South Korea: Integrating MODIS, ERA5-Land, and Soil Data. |
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| Authors: | Jang, Jiah1 (AUTHOR), Kim, Seung Hee2 (AUTHOR), Kafatos, Menas2,3 (AUTHOR), Cho, Jaeil3,4 (AUTHOR), Yoo, Gayoung4,5 (AUTHOR), Jeong, Sujong1,5 (AUTHOR), Lee, Yangwon1,2 (AUTHOR) modconfi@pknu.ac.kr |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p753. 36p. |
| Subjects: | Methane, Paddy fields, MODIS (Spectroradiometer), Machine learning, Soil profiles, Environmental mapping |
| Geographic Terms: | South Korea |
| Abstract: | Highlights: What are the main findings? An AutoML-based daily model integrating FLUXNET-CH4, ERA5-Land, MODIS, and HWSD data accurately predicts daily rice paddy CH4 fluxes (CC = 0.897 for 5-fold and CC = 0.819 for leave-one-year-out cross-validation). Variable importance shifts with temporal resolution: soil temperature dominates daily emissions (~50% contribution), whereas the hourly model exhibits a more multivariate structure, where vegetation status (NDVI) and soil moisture become more influential in regulating diurnal emission sensitivities and short-term dynamics. What are the implications of the main findings? The 500 m daily gridded CH4 maps, validated by robust temporal generalization (LOYO), support the refinement of national inventories and the implementation of spatially targeted mitigation strategies, such as precise water management timing. The multi-resolution framework provides a quantitative basis for selecting temporal resolution in operational emission monitoring, balancing mapping efficiency with process-level interpretability. Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine learning (AutoML) framework to compare their performance and process-level interpretability. The daily model demonstrated high predictive accuracy with correlation coefficients (CC) of 0.897 in 5-fold cross-validation and 0.819 in Leave-One-Year-Out (LOYO) cross-validation. Shapley Additive Explanations (SHAP) analysis revealed that while soil temperature is the dominant predictor for daily emissions (explaining ~50% of the variance), variable importance shifts significantly at finer resolutions. The hourly model exhibited a more complex multivariate structure. In this high-resolution context, although Normalized Difference Vegetation Index (NDVI) remains constant diurnally, its importance strengthens as a critical regulator of emission sensitivity, interacting with hourly meteorological fluctuations to capture short-term dynamics. The resulting 500 m daily gridded maps provide a robust foundation for national inventory refinement and spatially targeted mitigation planning. Our findings suggest that while the daily model offers optimal computational efficiency for long-term monitoring, the hourly model is superior for mechanistic understanding and detecting episodic emission events. This multi-resolution framework establishes an empirical basis for selecting appropriate temporal scales in operational greenhouse gas monitoring systems. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? An AutoML-based daily model integrating FLUXNET-CH4, ERA5-Land, MODIS, and HWSD data accurately predicts daily rice paddy CH4 fluxes (CC = 0.897 for 5-fold and CC = 0.819 for leave-one-year-out cross-validation). Variable importance shifts with temporal resolution: soil temperature dominates daily emissions (~50% contribution), whereas the hourly model exhibits a more multivariate structure, where vegetation status (NDVI) and soil moisture become more influential in regulating diurnal emission sensitivities and short-term dynamics. What are the implications of the main findings? The 500 m daily gridded CH4 maps, validated by robust temporal generalization (LOYO), support the refinement of national inventories and the implementation of spatially targeted mitigation strategies, such as precise water management timing. The multi-resolution framework provides a quantitative basis for selecting temporal resolution in operational emission monitoring, balancing mapping efficiency with process-level interpretability. Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine learning (AutoML) framework to compare their performance and process-level interpretability. The daily model demonstrated high predictive accuracy with correlation coefficients (CC) of 0.897 in 5-fold cross-validation and 0.819 in Leave-One-Year-Out (LOYO) cross-validation. Shapley Additive Explanations (SHAP) analysis revealed that while soil temperature is the dominant predictor for daily emissions (explaining ~50% of the variance), variable importance shifts significantly at finer resolutions. The hourly model exhibited a more complex multivariate structure. In this high-resolution context, although Normalized Difference Vegetation Index (NDVI) remains constant diurnally, its importance strengthens as a critical regulator of emission sensitivity, interacting with hourly meteorological fluctuations to capture short-term dynamics. The resulting 500 m daily gridded maps provide a robust foundation for national inventory refinement and spatially targeted mitigation planning. Our findings suggest that while the daily model offers optimal computational efficiency for long-term monitoring, the hourly model is superior for mechanistic understanding and detecting episodic emission events. This multi-resolution framework establishes an empirical basis for selecting appropriate temporal scales in operational greenhouse gas monitoring systems. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18050753 |
